HYBRID PALM OIL MILLS MAINTENANCE SYSTEM.

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ISSN: 2180 – 1053 Vol. 1 No. 1 July – December 2009 53

HYBRID PALM OIL MILLS

MAINTENANCE SYSTEM

Abd. Samad Hasan Basari1, Nanna Suryana Herman2, Mohammad Ishak Desa3

1,2

Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka, Locked Bag 1752, Pejabat Pos Durian Tunggal, 76109 Durian Tunggal, Melaka

{abdsamad1, nsuryana2}@utem.edu.my

3

Faculty of Computer Science and Information System, Universiti Teknologi Malaysia, 83100 Skudai, Johor

ABSTRACT

This paper proposes a technique that enhances snapshot model for cause of

failure and decision analysis in order to easily assist maintenance engineers

during identification and definition of the actual maintenance problem. The

technique is a hybrid of failure mode, effect and criticality analysis,

information technology and decision analysis into the snapshot model. A

tool that automates the hybrid of snapshot modelling for cause of failure

and decision analysis is also developed. This tool aims to ensure

maintenance engineers can conduct snapshot modelling with little or

without the help of operation research experts to facilitate in the cause of

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KEYWORDS

Hybrid system, maintenance, snapshot modelling

1.0 INTRODUCTION

The cause of failure analysis is the process of identifying, defining, and

diagnosing the maintenance problem. The main purpose of cause of failure

analysis is to avoid tackling the wrong problem. Generally this analysis

involves[1]:

a) Identifying the existence and location of the problem: Which are

recognising the symptoms, seriousness of the problem from the aspect of

cost, downtime as well as the size, and the areas of the fault in the

plant’s machines where the problems are most developed.

b) Determining the problem’s causes: The analysis of the problem’s causes

can be at structural or functional level. Consequently depending on the

level of causal analysis, different solution strategies may be generated.

c) Generating and determining possible solution strategies. Having

identified problem and its nature, location, causes and consequences,

then possible solution strategies could be developed or generated.

However, the data specified above are difficult to be found in any

organisation and also very tedious to be collected on a dynamic basis if

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ISSN: 2180 – 1053 Vol. 1 No. 1 July – December 2009 55 this reason the usages of a survey form for collecting such type of data on

periodic basis is suggested. At each failure or maintenance intervention, the

engineer registers the data related to the snapshot model in survey form.

Once finished, OR analyst collects back the survey form and starts the

analysis process. The results of the analysis, will be reported back to the

maintenance engineers which reveal the true status of the plant under the

study.

Despite the usefulness of the snapshot model as one of the important

tools for cause of failure and decision analysis, the implementation of the

model in large scale is doubtful. This is mostly due to, the scarcity and the

reliability of the data related to snapshot model, the problem of analysing

the data, and the problem of interpreting the results of the analysis to the

users (maintenance engineers).

2.0 THE PROPOSED HYBRID SYSTEM

The proposed approach of automating and augmenting snapshot model

aimed to complement such type of modelling. Enriched the various

techniques that have proven appropriate and possible in combining with

snapshot model could give a more effective, ease of use and practically

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2.1 Elements of Enhancement

The enhancement require the utilisation of the emerging information

technology (IT) and failure mode, effect and criticality analysis (FMECA).

In theory, IT and FMECA can be utilised to produce an enhanced snapshot

model. Once the data collection done, the analysis process needs further

techniques to be enriched. The technique called decision analysis is

introduced. The decision analysis will use analytic hierarchy process (AHP)

method and decision making grid (DMG) by utilising fuzzy logic rule base

(FLRB) method. Figure 1 shows the conceptual merger of above mentioned

techniques into the current snapshot model.

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2.2 Information Technology

The computer technology can increase the involvement of maintenance

engineers in the development of the snapshot model by allowing the

replacement of the survey form with a more general computer form that

contains feature of checking the validity and consistency of the data and can

be applied for different machines. It can also permit maintenance engineers

to carry out the snapshot analysis with little assistance of or without OR

analysts.

2.3 Failure Mode, Effect and Criticality Analysis

The failure mode and effect analysis that could combined with the snapshot

analysis include:

 Major fault areas and their modes. This analysis will analyse all number

of failures for each component according to their mode.

 Failure mode and their cause analysis. This analysis provides guidelines

and directions to which is need to be done for specific failure mode.

 Failure modes and their cost analysis. This analysis identifies the

consequences of each failure mode in term of the cost.

 Failure modes and their downtime. This analysis will lead to identify the

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 Failure modes and means of prevention analysis. This analysis identifies

the viable means of preventing each type of failure mode.

Criticality analysis (CA) is a procedure by which each potential failure

mode is ranked according to the combined influence of severity and

probability of occurrence. The procedure for obtaining the criticality

analysis is as follows [2]:

 The number of failure for each mode will be calculated from the

collected data.

 The total number for all the failure of the machine will be calculated.

 The failure mode frequency ratio (FMFR) will be calculated by dividing

the number of failure for each mode by the total number of failure for

the machines.

 Obtain the estimated probability of stopping, Ps, of the machine if the

failure in a given mode should occur.

 Obtain the component unreliability Q by subtracting the component’s

predicted reliability from 1 or 100 (if calculated in %).

 Calculated the Criticality CR = (FMFR) x (Ps) x (Q).

By using the above steps, criticality ranking will be conducted for the

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2.4 Decision Analysis

Decision analysis is particularly a techniques which is part of the framework

to achieve world class maintenance [3]. Among the established method to

implement decision analysis are AHP and DMG based on FLRB method.

The AHP is a decision support tool, which can lead the decision makers to

model a complex problem in a hierarchical structure showing the

relationship of the goal, objective (criteria), sub-objectives and alternatives

[4]. Figure 2 show the workflow of AHP process.

Figure 2 Analytic Hierarchy Process (AHP) Workflow

Setting up the hierarchy

Comparison of characteristics

Establish priority vector

Comparison of Alternatives

Establish priority vector for alternatives

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ISSN: 2180 – 1053 Vol. 1 No. 1 July – December 2009 60 There are four major steps to calculate AHP which are:

1) Setting up the hierarchy: The first step in AHP is to develop hierarchy by

breaking the problem down into components. This level is also known as

design phase. The three major level of hierarchy are the goal, objectives and

alternatives.

2) Comparison of characteristics and establish priority vector:

Characteristics refer to the objectives or criteria that located in the second

level of the hierarchy. In this phase, it is known as evaluation phase.

Decision maker needs to perform comparison between each objective in a

one-to-one (N x N) matrix form. Pair wise comparison is used to determine

the relative importance of each alternative in term of each criterion. The pair

wise comparison expresses the qualitative answer of a decision maker into

some numbers, which is easy to manipulate in the calculation and solve the

problem of inconsistency unit of measurement for each criterion. Table 1

showed the proposed scale where the scale member set is {9, 8, 7, 6, 5, 4, 3,

2, 1, 1/2, 1/3, 1/4, 1/5, 1/6, 1/7, 1/8, 1/9}.

By referring to the above standard scale, a matrix of characteristic

(objectives) can be constructed. For consistency, it is necessary to set aji

=1/aij (this state the obvious fact that if objective 1 is slightly more

important than objective 3, than the objective 3 is slightly less important

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Table 1 Scale of Relative Importance

Intensity of Importance

Definition Explanation

1 Equal importance Two activities contribute

equally to the objective

3 Weak importance of

one over another

Experience and judgement slightly favour one activity over another

5 Essential or strong

importance

Experience and judgement strongly favour one activity over another

Hence the concept of putting values in a matrix conform the following rules:

a) The equal attribute in the matrix is put as 1 (diagonal).

b) The decision maker only needs to fill the upper right triangle of the

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ISSN: 2180 – 1053 Vol. 1 No. 1 July – December 2009 62 c) For the lower left triangle of the matrix, the value should be the

inverse of the corresponding cell in upper right.

3) Comparison of alternatives and establish priority vector for alternatives:

The previous steps are determined the weight of each objectives, so the next

step is to determine how well each alternative score on each objective. The

process of calculation is almost similar with the previous step where a pair

wise comparison matrix for each objective is constructed by referring to the

scale.

4) Obtaining the overall ranking: The final step is to obtain a vector of

overall scores for each alternative, which can accomplish by multiplying the

weight calculated by each alternative associated to each of the criteria. The

first ranked alternative will have the highest weight (highest priority).

One foundation of the AHP is the observation that the human

decision-making is not always consistent. Consistency suffers when the criteria being

compared are subjectively in nature. The AHP provides a standard by which

the degree of consistency can be measured. If inconsistency exceeds an

established threshold, then participants can re-examine their judgements. In

the AHP, the pair wise comparisons in a judgement matrix are considered to

be adequately consistent if the corresponding consistency ratio (CR) is less

than 10%. First, the columns in the judgement matrix A, multiply with the

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ISSN: 2180 – 1053 Vol. 1 No. 1 July – December 2009 63 yield an approximation of the maximum eigenvalue, denoted by max (an

eigenvalue of a square matrix A is a scalar c such that Aw = cw holds for

some nonzero vector w).

Then the consistency index (CI) value is calculated by using formula

CI = (max – n)/(n-1). Next, the consistency ratio (CR) is computed by

dividing the CI value by the random index (RI). The CR is the average CI of

sets of judgements (from a 1 to 9 scale) for randomly generated reciprocal

matrices. The consistency index is shown in Table 2.

For a perfectly consistent decision maker, each ratio in Step 2 equal to n.

This implies that a perfectly consistent decision maker has CI = 0. The

values of RI in Table 2 give the average value of CI if the entries in, for

example A were chosen at random (subject to the constraints that aij's must

equal 1, and aij = 1/aji). If the ratio of CI to RI is sufficiently small, then the

decision maker's comparison is probably consistent enough to be useful. If

CI/RI<0.10, then the degree of consistency is satisfactory, whereas if CI/RI

> 0.10, serious inconsistencies exist and AHP may not yield any meaningful

results [5].

The features to enhance the snapshot model are:

1) First level-Criteria evaluation: This steps need the decision maker

prioritises his/her preferences on different criteria such as fault mode, effect,

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Table 2 Random Index/Random Consistency Index for Different Value of n

2) Second level-Sub criteria evaluation: This steps need the decision maker

prioritises his/her preferences on different sub criteria such as number of

fault, machine downtime, cost and criticality.

3) Third level-Alternatives selection: The machines are ranked according to

their weights. Weights are obtained through running an AHP algorithm in an

absolute mode and hence a consistency ratio of value zero is assured.

The above mentioned three level of AHP method is a complimentary of

three type of analysis provided by snapshot model which are major fault

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ISSN: 2180 – 1053 Vol. 1 No. 1 July – December 2009 65 FMECA features called fault mode, effect and criticality analysis embedded

to snapshot model, they also will be added features to decision analysis

process.

The three steps of the fuzzy controller are Fuzzification, Rule evaluation

(Inference) and Defuzzification [6]. Each of these steps is described below:

1) First step-fuzzification: The first step in the fuzzy controller is the

fuzzification process. The membership function, universe of discourse U, is

the classifications that are considered in the problem. It is assumed that both

frequency and downtime can be classified into `High', `Medium' and `Low'.

2) Second step-rule evaluation: The rule evaluation step can also be

explained as an input-output system. In this step, inputs are expert rules, and

fuzzy inputs obtained from the first step (that is values of m), while outputs

are fuzzy values of maintenance actions to be carried out. Given two

variables of frequency and downtime with each having three subsets of Low,

Medium, and High, then one needs at least nine (3x3) rules to describe the

model (system). These rules are in the form of IF . . . THEN . . . statements.

Examples of maintenance prescriptions are as follows:

a) Operate To Failure (OTF)

b) Fixed Time Maintenance (FTM)

c) Skill Levels Upgrade (SLU)

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ISSN: 2180 – 1053 Vol. 1 No. 1 July – December 2009 66 e) Design Out Maintenance (DOM)

A summary of the application of each action, based on the values of

Frequency (Fr) and Downtime (Dt), is given in Table 3. An example of a

rule can be `IF downtime is low and frequency is high, THEN improve

operators skill. This rule can be written as follows:

IF frequency is HIGH and downtime is LOW THEN S. L. U (Rule 7)

Rule 7 is shown in the third row, and first column in Table 3. The summary

of rules is presented in Table 3.

Table 3 Summary of Rules for Maintenance Actions

Dt

Once rules are constructed, and given the values of the fuzzy inputs for

(mfl, mfm, mfh, mdl, mdm, mdh) one can apply the minimum and

maximum (AND & OR Zadeh) inference computations.

3) Third step-defuzzification: This is the final step in the fuzzy controller.

This process is based on the idea of deriving a crisp value from a fuzzy

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ISSN: 2180 – 1053 Vol. 1 No. 1 July – December 2009 67 gravity of the area under the curve of the function. Given the cost function

of each maintenance action, one can arrange the maintenance actions, the

fuzzy output, and the cost scale function. The feedback mechanism offered

by the rules grid or DMG of fuzzy logic, as shown in Table 3, in addition to

the feedback already offered in AHP in the form of consistency ratio,

provides an effective performance.

The above-mentioned FLRB method will be used as an enhancement of

snapshot model features called prevention action analysis.

3.0 THE CASE STUDY AND RESULT

This case study demonstrates the application of the above-mentioned

techniques and its effect on maintenance performance. This company is used

as a pilot study in order to test whether the system meets the user

expectation and preference. A number of experts were interviewed and

proposed during the design and develop the targeted system.

3.1 Company Background

The company is a palm oil mills (POM), which the main job is extracting

the Fresh Fruit Bunches (FFB) to Crude Palm Oil (CPO). In this particular

company there are about 50 major machines or plants. Since the aim of this

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ISSN: 2180 – 1053 Vol. 1 No. 1 July – December 2009 68 action, the case study related to an old POM, which are operated more than

ten years and use a conventional method of cause of failure and decision

analysis techniques.

3.2 The Result

Most of the maintenance information found at POM at the time of the study

commenced is originated from the unstructured daily and lubricant report.

The unstructured daily report only has the date of the report, time of the

report and the description of works. The lubricant report just gives the

machine that need a top up or change the lubricant oil, the quantity of oil

needed and a description of work or problem occurs that might cause the

need to top up or change the lubricant oil.

The snapshot model will be built based on the data collected from POM

concerning the most problematic machine namely, Screw Press. Example of

the result using the hybrid cause of failure and decision analysis techniques

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Table 4 The Combined Major Fault Area with the Number of Fault, Criticality, Cost and Downtime Analysis for the

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Table 5 Final Result based on the AHP Method in the Decision Analysis Technique to Select the Most Critical Components for the Period from

1.8.05 to 30.9.05

Table 6 Final Result based on the FLRB Method in the Decision Analysis Technique to Select Prevention Action for the

Period from 1.8.05 to 30.9.05

No Code Name Policy Code Description

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4.0 CONCLUSION

It is recognised the importance of the snapshot model as a tool for cause of

failure and decision analysis. The recent development in the computer

technology in terms of speed and capacity coupled with the successful

research in the human computer interaction play considerable role in the

development of a successful tool that capable of constructing snapshot

model. From the result, it shows that the use of hybrid maintenance cause of

failure and decision analysis could significantly improve the decision

context by adding the features of snapshot model. In term of efficiency of

decision-making process, the result shows that the reduction of time to reach

decision among the decision makers. The hybrid cause of failure and

decision analysis techniques could not deny the use of human judgments

during the survey that have been conducted. Further enhancement could be

done by embedding the techniques with the computerised maintenance

management system (CMMS). The quality of data also could be the major

issues and it could be done by using the automated data capturing

techniques such as using condition monitoring method.

5.0 ACKNOWLEDGEMENT

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6.0 REFERENCES

[1] L. Liu (1997), Development of an Integrated System for Machine Fault

Diagosis, PhD in Industrial Engineering, University of Houstan.

[2] D. Kececioglu (1991), Reliability engineering handbook, Englewood

Cliffs, NJ: Prentice Hall.

[3] A.W. Labib (1998), World Class Maintenance using Computerised

Maintenance Management System, Journal of Quality in Maintenance

Engineering, Vol. 4, pp. 66-75.

[4] T.L. Saaty (1977), A Scaling Method for Priorities in Hierarchical

Structures, Journal of Mathematical Psychology, Vol. 15, pp. 234-281.

[5] T.L. Saaty (1990), How to Make a Decision: The Analytic Hierarchy

Process, European Journal of Operational Research, Vol. 48, pp. 9-26.

[6] R.K. Sharma, D. Kumar and P. Kumar (2007), FM - A Pragmatic Tool

to Model, Analyse and Predict Complex Behaviour of Industrial Systems,

Engineering Computations: International Journal for Computer-Aided